1. Technical Field
The present disclosure relates to a neuron system, and more specifically to a neuromorphic system implemented in hardware and a method for operating the same.
2. Description of the Related Art
A biological brain contains about hundreds of billions of nerve cells or neurons, which form a complicated neuron network. Each neuron is connected to thousands of other neurons through synapses to communicate signals, achieving intelligence required by living things.
A neuron is a structural and functional unit of a neuron system, as well as a basic unit for transferring information. A synapse is a structure that permits a neuron to pass a signal to another. An axon of a neuron and a dendrite of another neuron are connected to a synapse. A neuromorphic system is a semiconductor circuit that mimics the information processing by a brain by implementing an artificial neuron system resembling a biological neuron system at neuron level. Such a neuromorphic system may be implemented as an electronic circuit using semiconductors.
Such a neuromorphic system may be effectively utilized in implementing an intelligent system capable of adapting itself to uncertain environment, like a biological brain. For example, a neuromorphic system may be used as an interface for awareness and inferring such as character recognition, voice recognition, risk awareness, real-time high-speed signal processing, etc. Moreover, a neuromorphic system may be applied to computers, robots, home appliances, small mobile devices, security and monitoring systems, intelligent vehicles, etc.
A neuromorphic system may be implemented using a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm, etc. The supervised learning algorithm has an advantage in that it can be implemented in hardware with simple configuration, but has a disadvantage in that it takes much time and cost in configuring learning data for every possible case. The unsupervised learning algorithm has an advantage in that it can achieve high learning efficiency with less learning data by virtue of clustering. However, it requires complicated calculations and is time-consuming since many factors are used in non-linear calculations. To overcome such disadvantages the algorithms suffer, there has been proposed a semi-supervised learning algorithm that employs the unsupervised learning algorithm and the unsupervised learning algorithm together.
The semi-supervised learning algorithm exhibits high learning efficiency with less learning data. However, it still requires a large amount of calculations and thus is time-consuming. For example, it often takes twenty-four hours for calculation. Accordingly, it is impractical to implement the semi-supervised learning algorithm in software.